Utilize Query History and Caching to Reduce Development Time and Query Latency DATA-ANALYST-ASSOCIATE Practice Questions (Page 5)

Questions 21–25

1. 21application · medium

   An analyst is comparing two versions of a query in Query History. Version A has a shorter execution time but a much higher 'Scan Bytes' metric than Version B. Version B has a longer execution time but scans far fewer bytes. What is the most likely explanation for this difference?

   • AVersion A is reading from a larger, unpartitioned table, while Version B reads from a smaller, filtered subset.
   • BVersion A was run on a larger, more powerful cluster than Version B.
   • CVersion A benefited from result caching, while Version B did not.
   • DVersion A used disk caching, while Version B used only memory caching.
   Reveal Solution Discussion Select an answer first
2. 22foundation · easy

   What is the primary purpose of result caching in Databricks SQL?

   • ATo automatically optimize query plans for future runs
   • BTo store frequently accessed data in memory for faster scanning
   • CTo store query results so that repeated identical queries can return results faster without re-executing
   • DTo compress query results to save storage space
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   A data analyst sees a query in Query History with a high 'Execution time' but a low 'Scan bytes'. What does this combination most likely indicate?

   • AThe query was cancelled by the user
   • BThe result size was very large
   • CThe query spent time on computation or waiting, not on reading large amounts of data
   • DThe query read a large amount of data from storage
   Reveal Solution Discussion Select an answer first
5. 24application · medium

   An analyst frequently runs a complex SQL query with different date filters to explore sales data. They notice that changing the date filter causes the query to run from scratch every time. What is the most effective way to improve performance for these repeated explorations?

   • AUse a parameterized query in a dashboard so the filter changes don't invalidate the cache.
   • BRun the query once and save the result as a CSV file to share with the team.
   • CPre-aggregate the data into a summary table and query that table instead.
   • DCreate a temporary view of the base data and query that view with different filters.
   Reveal Solution Discussion Select an answer first
6. 25foundation · easy

   When does Databricks automatically invalidate a cached query result?

   • AWhen the cluster is restarted
   • BWhen the underlying data used by the query changes
   • CWhen the query is run more than once
   • DWhen the query is run by a different user
   Reveal Solution Discussion Select an answer first